A system includes a processor configured to create a rule repository instance. The rule repository instance specifies a plurality of different procedures that facilitate specifying a sequence of transformer rules by cascading each of the procedures together using a dot notation format. The processor configures the rule repository instance with a plurality of transformer rules using the dot notation format. The processor receives data from a file arranged according to a first structured data format. The processor executes the sequence of transformer rules to convert data elements in the file to a second structured data format. The processor then provides for display or storage the data as converted into the second structured data format by the sequence of transformer rules.
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3. The system according to claim 2, wherein the path parameter corresponds to a Java object, a JavaScript Object Notation (JSON) path or an Extensible Markup Language (XML) path that specifies the set of data elements to be converted.
A system for data conversion processes a path parameter that specifies a set of data elements to be converted. The path parameter can correspond to a Java object, a JavaScript Object Notation (JSON) path, or an Extensible Markup Language (XML) path. This allows the system to dynamically identify and extract the relevant data elements from structured data formats such as Java objects, JSON documents, or XML files. The extracted data elements are then converted into a desired output format, enabling seamless integration between different data representations. The system supports flexible and precise data extraction by leveraging path-based navigation, ensuring that only the specified elements are processed. This approach simplifies data transformation tasks, reduces manual intervention, and improves accuracy in converting structured data across various formats. The system is particularly useful in applications requiring interoperability between different data models or systems, such as enterprise software, web services, and data integration platforms. By using standardized path expressions, the system ensures consistency and reliability in data conversion operations.
5. The system according to claim 2, wherein the operation associated with the second procedure corresponds to one of: a math operation that modifies the value of the key/value pair data structure according to a computation, a text operation that modifies the value of the key/value pair data structure by trimming a portion of the value, or a concatenation operation that modifies the value of the key/value pair data structure by adding another value to the value.
A system for processing key/value pair data structures includes a method for executing procedures that modify the values within these structures. The system performs operations on key/value pairs stored in a database or memory, where each operation is triggered by a predefined procedure. The operations include mathematical computations that alter the value based on arithmetic or logical calculations, text operations that trim or modify portions of the value, and concatenation operations that append additional values to the existing value. These modifications are applied to the stored key/value pairs to update their content dynamically. The system ensures that the operations are executed in a controlled manner, allowing for flexible data manipulation while maintaining data integrity. This approach is useful in applications requiring real-time data processing, such as databases, caching systems, or data transformation pipelines, where values need to be updated or transformed based on specific rules or conditions. The system supports various types of operations, including arithmetic modifications, text processing, and value concatenation, providing a versatile tool for managing and transforming key/value data structures efficiently.
6. The system according to claim 1, wherein each of the procedures returns a reference to the rule repository instance.
A system for managing and executing procedural rules in a computing environment addresses the challenge of efficiently organizing, retrieving, and applying dynamic rule sets across multiple processes. The system includes a rule repository that stores a collection of rules, each defined by a set of conditions and corresponding actions. Procedures within the system are designed to interact with the rule repository, allowing for the retrieval, evaluation, and execution of rules based on input data. Each procedure is configured to return a reference to the rule repository instance, enabling subsequent procedures or processes to access the same rule set without redundant retrieval operations. This ensures consistency and reduces computational overhead by avoiding repeated access to the repository. The system may also include mechanisms for updating the rule repository dynamically, allowing rules to be modified or added without interrupting ongoing processes. The procedures may further include validation steps to verify the integrity and applicability of the rules before execution. By maintaining a centralized rule repository and providing direct references to it, the system enhances efficiency, scalability, and maintainability in environments where rule-based decision-making is critical.
7. The system according to claim 1, wherein the first structured data format corresponds to one of: JavaScript Object Notation (JSON), Extensible Markup Language (XML), a JAVA object, or a database table, and wherein the second structured data format corresponds to one of: JSON, XML, a JAVA object, or a database table.
A system converts data between different structured formats to facilitate interoperability between systems that use incompatible data representations. The system addresses the challenge of integrating disparate systems that rely on different structured data formats, such as JSON, XML, Java objects, or database tables, which often leads to inefficiencies and errors in data exchange. The system includes a data transformation module that processes input data in a first structured format and converts it into a second structured format, ensuring compatibility between systems. The transformation module may use predefined mapping rules or dynamic conversion logic to handle the conversion process. The system also includes an interface module that manages the input and output of data, ensuring seamless interaction with external systems. The conversion process preserves the integrity and structure of the data, allowing for accurate and reliable data exchange. This system is particularly useful in environments where multiple systems with different data formats must communicate, such as in enterprise software, cloud computing, or data integration platforms. The flexibility of the system allows it to support various combinations of input and output formats, making it adaptable to diverse use cases.
8. The system according to claim 1, wherein the set of data elements correspond to one or more of: scalar values, records, objects, or arrays of objects.
The invention relates to a data processing system designed to handle diverse data structures efficiently. The system is configured to process a set of data elements, where these elements can take various forms, including scalar values, records, objects, or arrays of objects. This flexibility allows the system to manage different types of data seamlessly, ensuring compatibility with a wide range of data formats and structures commonly encountered in computing environments. The system is particularly useful in applications where data heterogeneity is a challenge, such as in databases, data analytics, or software development, where different data types must be processed together. By supporting multiple data element types, the system simplifies integration and reduces the need for complex data conversion or transformation steps. This capability enhances interoperability and streamlines data workflows, making it easier to handle complex datasets without sacrificing performance or accuracy. The system's ability to process scalar values, records, objects, and arrays of objects ensures that it can adapt to various data models and use cases, providing a robust solution for modern data-intensive applications.
9. The system according to claim 1, wherein cascading the procedures together using the dot notation format comprises specifying that the plurality of different procedures specified in the rule repository instance be performed in a particular order, one after another.
This invention relates to a system for executing cascading procedures in a rule-based processing environment. The system addresses the challenge of efficiently managing and executing multiple procedures in a predefined sequence, ensuring that each procedure is performed in a specific order, one after another. The system includes a rule repository instance that stores a plurality of different procedures, each designed to perform distinct tasks. The procedures are cascaded together using a dot notation format, which explicitly defines the sequence in which the procedures should be executed. This cascading mechanism ensures that the procedures are performed in the exact order specified, eliminating the need for manual intervention or complex control logic. The system dynamically retrieves the procedures from the rule repository and executes them in the defined sequence, improving efficiency and reducing errors in rule-based processing workflows. The dot notation format provides a clear and concise way to represent the procedural order, making the system adaptable to various rule-based applications. The invention enhances the flexibility and reliability of rule-based systems by ensuring that procedures are executed in the intended sequence, thereby improving overall system performance and accuracy.
12. The computer-implemented method according to claim 11, wherein the path parameter and field parameters are text strings.
A system and method for processing data queries in a database environment involves generating and executing queries based on user-provided parameters. The method includes receiving a path parameter and one or more field parameters, where these parameters are text strings. The path parameter specifies a location or structure within a data source, such as a database or file system, while the field parameters identify specific data elements or attributes to be retrieved. The system parses these text-based parameters to construct a query, which is then executed against the data source to retrieve the requested information. The method may also include validating the parameters to ensure they conform to expected formats or constraints before query execution. This approach allows users to dynamically specify data retrieval operations using simple text inputs, reducing the need for complex query syntax and improving accessibility for non-technical users. The system may further support error handling and feedback mechanisms to guide users in correcting invalid or malformed inputs. The method is particularly useful in applications requiring flexible and user-friendly data access, such as reporting tools, analytics platforms, or database management systems.
13. The computer-implemented method according to claim 11, wherein the path parameter corresponds to a Java object, a JavaScript Object Notation (JSON) path or an Extensible Markup Language (XML) path that specifies the set of data elements to be converted.
This invention relates to data processing systems that convert data between different formats, such as Java objects, JSON, and XML. The problem addressed is the need for a flexible and efficient way to specify which data elements should be extracted or transformed during such conversions. The invention provides a method that uses a path parameter to define the set of data elements to be converted. The path parameter can correspond to a Java object, a JSON path, or an XML path, allowing the method to dynamically select and process specific data elements based on the path specification. This approach enables precise control over data conversion processes, ensuring only the required elements are processed, which improves efficiency and reduces unnecessary computations. The method is particularly useful in systems where data must be transformed between different formats while maintaining accuracy and minimizing resource usage. By supporting multiple path types, the invention provides compatibility with various data structures and formats, making it adaptable to different use cases. The path parameter can be used to navigate and extract data from nested or complex data structures, ensuring that only the relevant portions are converted. This method enhances the flexibility and efficiency of data processing systems that handle multiple data formats.
14. The computer-implemented method according to claim 11, wherein the second procedure that corresponds to the adapter for performing an operation is cascaded to the fourth procedure, wherein the fourth procedure takes a further field parameter that corresponds to the field parameter of the fourth procedure, wherein the method comprises: modifying the value associated with the key/value pair data structure stored in the memory according to an operation associated with the adapter.
This invention relates to a computer-implemented method for processing data using cascaded procedures in a data processing system. The method addresses the challenge of efficiently modifying data stored in a key/value pair data structure by leveraging adapters and cascaded procedures to perform operations on the data. The method involves a system where a second procedure, associated with an adapter for performing a specific operation, is cascaded to a fourth procedure. The fourth procedure accepts a field parameter that corresponds to a field parameter of the fourth procedure itself. The method further includes modifying the value associated with a key/value pair stored in memory based on an operation defined by the adapter. The cascading of procedures allows for sequential or conditional execution of operations, enabling flexible and dynamic data processing workflows. The use of adapters ensures that the operations are modular and can be easily integrated or swapped without altering the core logic of the procedures. This approach enhances system adaptability and maintainability while ensuring efficient data manipulation. The method is particularly useful in systems requiring dynamic data transformations, such as real-time data processing, configuration management, or workflow automation.
15. The computer-implemented method according to claim 14, wherein the operation associated with the second procedure corresponds to one of: a math operation that modifies the value of the key/value pair data structure according to a computation, a text operation that modifies the value of the key/value pair data structure by trimming a portion of the value, or a concatenation operation that modifies the value of the key/value pair data structure by adding another value to the value.
This invention relates to computer-implemented methods for processing key/value pair data structures, addressing the need for efficient and flexible data manipulation within such structures. The method involves performing operations on key/value pairs stored in a database or data store, where these operations modify the values associated with the keys. Specifically, the method includes executing a second procedure that applies one of several types of operations to the value of a key/value pair. These operations include mathematical computations that alter the value based on a predefined calculation, text operations that modify the value by trimming a portion of the text, or concatenation operations that append another value to the existing value. The method ensures that the data stored in the key/value pair structure is dynamically updated according to the specified operation, enabling real-time data transformation and processing. This approach enhances data management by allowing for flexible and programmable modifications to stored values, improving efficiency in applications requiring dynamic data manipulation. The invention is particularly useful in systems where data needs to be frequently updated or transformed, such as in databases, data processing pipelines, or real-time analytics platforms.
16. The computer-implemented method according to claim 10, wherein each of the procedures returns a reference to the rule repository instance.
A system and method for managing rule repositories in a computing environment addresses the challenge of efficiently accessing and updating rule sets across distributed applications. The invention provides a centralized rule repository that stores and manages rules used by various applications, ensuring consistency and reducing redundancy. The method involves creating a rule repository instance that serves as a container for rules, where each rule is associated with a unique identifier and metadata describing its purpose and usage. The system allows applications to query the repository to retrieve specific rules or rule sets based on predefined criteria, such as rule type or application context. Additionally, the system supports dynamic updates to the repository, enabling rules to be modified or added without disrupting ongoing operations. The method includes procedures for validating rules before they are stored in the repository to ensure they meet specified criteria, such as syntax correctness or compliance with business logic. Each procedure in the system returns a reference to the rule repository instance, allowing applications to maintain a persistent connection to the repository for subsequent operations. This ensures efficient access and reduces the overhead of repeatedly establishing connections. The invention improves rule management by centralizing rule storage, simplifying updates, and ensuring consistent access across multiple applications.
17. The computer-implemented method according to claim 10, wherein the first structured data format corresponds to one of: JavaScript Object Notation (JSON), Extensible Markup Language (XML), a JAVA object, or a database table, and wherein the second structured data format corresponds to one of: JSON, XML, a JAVA object, or a database table.
This invention relates to a computer-implemented method for converting structured data between different formats. The method addresses the challenge of efficiently transforming data between incompatible structured formats, which is common in software systems that integrate disparate data sources or services. The method involves receiving input data in a first structured format, such as JavaScript Object Notation (JSON), Extensible Markup Language (XML), a JAVA object, or a database table. The input data is then processed to extract its underlying information, which is subsequently reorganized and encoded into a second structured format. The second format may also be JSON, XML, a JAVA object, or a database table, allowing for flexible interoperability between different data representations. The method ensures that the transformed data retains its integrity and structure while adapting to the requirements of the target system. This approach simplifies data exchange in heterogeneous environments, reducing the need for manual conversion or custom middleware solutions. The technique is particularly useful in applications requiring seamless integration of diverse data sources, such as enterprise systems, cloud services, or distributed computing platforms.
18. The computer-implemented method according to claim 10, wherein the set of data elements correspond to one or more of: scalar values, records, objects, or arrays of objects.
This invention relates to a computer-implemented method for processing data elements in a database or data storage system. The method addresses the challenge of efficiently managing and querying diverse data types, which can include scalar values, records, objects, or arrays of objects. The system dynamically identifies and categorizes these data elements to optimize storage, retrieval, and manipulation operations. By recognizing the structure and type of each data element, the method ensures compatibility with various data processing tasks, such as filtering, sorting, or aggregation, without requiring manual intervention or predefined schemas. The approach enhances flexibility and performance in handling heterogeneous data, making it suitable for applications like real-time analytics, database management, or distributed computing environments. The method may also integrate with existing data processing frameworks to extend their capabilities for mixed data types. The solution improves efficiency by reducing the need for type conversion or schema validation, while maintaining accuracy in data operations.
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November 6, 2019
December 20, 2022
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